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Water as a medium poses a number of challenges for robots, limiting the progress of research in underwater robotics vis-a-vis ground or aerial robotics. The primary challenges are satellite based positioning and radio communication being unusable due to hi ...

In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...

The upsurge in the number of web users over the last two decades has resulted in a significant growth of online information. Recommenders are machine learning approach and are becoming one of the main ways to navigate the Internet. They recommend appropria ...

We propose a new randomized coordinate descent method for a convex optimization template with broad applications. Our analysis relies on a novel combination of four ideas applied to the primal-dual gap function: smoothing, acceleration, homotopy, and coord ...

In recent years, global navigation satellite system (GNSS)-based navigation in high earth orbits (HEOs) has become a field of research interest since it can increase the spacecraft’s autonomy, thereby reducing the operating costs. However, the GNSS availab ...

Data augmentation is the process of generating samples by transforming training data, with the target of improving the accuracy and robustness of classifiers. In this paper, we propose a new automatic and adaptive algorithm for choosing the transformations ...

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Easily Deployable Underwater Acoustic Navigation System for Multi-Vehicle Environmental Sampling Applications

Mean-Field methods for Structured Deep-Learning in Computer Vision

Privacy in Recommender Systems

Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

GPS Based Navigation Performance Analysis within and beyond the Space Service Volume for Different Transmitters’ Antenna Patterns

Adaptive data augmentation for image classification